A web tool for finding gene candidates associated with experimentally induced arthritis in the rat

Distribution of candidate genes for experimentally induced arthritis in rats

Background

Rat models are frequently used to link genomic regions to experimentally induced arthritis in quantitative trait locus (QTL) analyses. To facilitate the search for candidate genes within such regions, we have previously developed an application (CGC) that uses weighted keywords to rank genes based on their descriptive text. In this study, CGC is used for analyzing the localization of candidate genes from two viewpoints: distribution over the rat genome and functional connections between arthritis QTLs.

Methods

To investigate if candidate genes identified by CGC are more likely to be found inside QTLs, we ranked 2403 genes genome wide in rat. The number of genes within different ranges of CGC scores localized inside and outside QTLs was then calculated. Furthermore, we investigated if candidate genes within certain QTLs share similar functions, and if these functions could be connected to genes within other QTLs. Based on references between genes in OMIM, we created connections between genes in QTLs identified in two distinct rat crosses. In this way, QTL pairs with one QTL from each cross that share an unexpectedly high number of gene connections were identified. The genes that were found to connect a pair of QTLs were then functionally analysed using a publicly available classification tool.

Results

Out of the 2403 genes ranked by the CGC application, 1160 were localized within QTL regions. No difference was observed between highly and lowly rated genes. Hence, highly rated candidate genes for arthritis seem to be distributed randomly inside and outside QTLs. Furthermore, we found five pairs of QTLs that shared a significantly high number of interconnected genes. When functionally analyzed, most genes connecting two QTLs could be included in a single functional cluster. Thus, the functional connections between these genes could very well be involved in the development of an arthritis phenotype.

Conclusions

From the genome wide CGC search, we conclude that candidate genes for arthritis in rat are randomly distributed between QTL and non-QTL regions. We do however find certain pairs of QTLs that share a large number of functionally connected candidate genes, suggesting that these QTLs contain a number of genes involved in similar functions contributing to the arthritis phenotype.

Ranking candidate genes in rat models of type 2 diabetes

Background

Rat models are frequently used to find genomic regions that contribute to complex diseases, so called quantitative trait loci (QTLs). In general, the genomic regions found to be associated with a quantitative trait are rather large, covering hundreds of genes. To help selecting appropriate candidate genes from QTLs associated with type 2 diabetes models in rat, we have developed a web tool called Candidate Gene Capture (CGC), specifically adopted for this disorder.

Methods

CGC combines diabetes-related genomic regions in rat with rat/human homology data, textual descriptions of gene effects and an array of 789 keywords. Each keyword is assigned values that reflect its co-occurrence with 24 different reference terms describing sub-phenotypes of type 2 diabetes (for example "insulin resistance"). The genes are then ranked based on the occurrences of keywords in the describing texts.

Results

CGC includes QTLs from type 2 diabetes models in rat. When comparing gene rankings from CGC based on one sub-phenotype, with manual gene ratings for four QTLs, very similar results were obtained. In total, 24 different sub-phenotypes are available as reference terms in the application and based on differences in gene ranking, they fall into separate clusters.

Conclusion

The very good agreement between the CGC gene ranking and the manual rating confirms that CGC is as a reliable tool for interpreting textual information. This, together with the possibility to select many different sub-phenotypes, makes CGC a versatile tool for finding candidate genes. CGC is publicly available at .